1. Field of the Invention
This invention relates to a discrete event simulation and method of model development for operations and support of weapons systems and more specifically to the creation and use of common attributes and a library of common blocks and sub-models to model a service use profile (SUP).
2. Description of the Related Art
There is ongoing need to affordably sustain levels of readiness for existing weapon systems, and to design new weapon systems that can be affordably sustained over a specified life-cycle. It follows that there are analytical needs for existing and future fielded weapon systems, which account for operations and support policies, weapon reliability, maintenance concept (maintenance location, weapon spares, spare weapon components, test equipment, and support equipment) and support infrastructure. These factors have combined consequences on the effectiveness of the weapon system by directly impacting the operational and stockpile availability of the population and the reliability of weapons at the time they are required for actual use.
A thorough analysis should account for: exposure of sub-populations to different environments and the resultant effect on their reliability in differing environments; possible improvements and/or degradation in inherent reliability over time; effectiveness and reliability of the test equipment; tracking multiple variants of weapons with differing reliability characteristics or maintenance procedures through the support system; the possibility of weapon inventories being taxed through multiple global engagements, and determining effects of retrofit or recall programs; the possibility of future retrofit requiring recall of all or a portion of the inventory; and the increasing complexities of weapon systems leading to more complicated field build-up, storage, maintenance, testing, and deployment procedures. The analysis that integrates the above factors should provide: expected quantity and likely time of repairs from the field; operational and stockpile availability estimates; prediction of reliability requirement of weapon hardware; prediction of maintainability characteristics for weapon hardware (e.g., hardware accessibility & modularity); spare weapon and spare parts requirements at repair locations; and flow volumes of weapons and weapon parts through logistics pipeline infrastructure (prediction of the quantity of any cost-incurring event of interest).
Within the defense industry and government labs, weapon support systems are typically modeled with whatever tools and expertise are at hand at the time the model is needed. This can lead to inconsistencies between studies. Also the problem is often broken up for simplicity. For example, a fielded spares analysis may be done independently of an availability analysis. This piecemeal approach can lead to inconsistency and inaccuracies as different ground rules and assumptions can be used between the models. Even if careful consideration was given to using consistent ground rules, the assumptions for bridging information between modeled components of a larger system can oversimplify the effect of complex interdependencies of those components within the larger system.
It is also not uncommon within industry and government, even among some technical experts and engineers, to oversimplify the problem. For estimating future maintenance loads over a weapon program's life-cycle, for example, it is often thought that a reasonable estimate of non-operational reliability is the most significant factor. Although important, non-operational reliability is often not the most significant factor. Consider this: Given an inventory of 1000 stored weapons, each with storage MTBF of 1,000,000 hours (106 hrs), if after 5 years (43,800 hrs) all are tested, the number of repairs will be 1,000*(43,800/106)=44 weapons. However, if policy establishes that entire inventory is to be tested 200 at a time, every year (8,760 hrs) for five years, the number of repairs will be for Σn-1 to 5 200*(n*8,760/106)=26 weapons.
Grossly over-simplistic by not accounting for any complicating factors mentioned previously, this example shows how just one aspect, test policy, can greatly influence the maintenance load outcome. Also, revisiting the point made earlier about interdependencies, fewer failures detected affects other aspects of the system, such as population availability, logistics pipeline throughput (effecting transportation and other infrastructure), and the reliability of a weapon up to the point of actual use. The one change in test policy has a domino affect on many aspects of the system. Clearly, an integrated approach should be taken when analyzing an Operations and Support system.
If the problem is attacked as a whole system, tools traditionally used include spreadsheets or conventional computer programs. Mechanized arrays of equations implemented on a computer, otherwise known as spreadsheets, can easily oversimplify by either not modeling enough detail within support process (detail that can have unexpected impact on the output of the model), not capturing the dynamic aspects (e.g., surges in inventory demand), or not capturing the stochastic nature of the problem. If built to accommodate all pertinent detail, dynamic, and probabilistic aspects of the system, the spreadsheet model will likely be very large and complicated, so as to be unwieldy, hard to use, difficult to modify and difficult for others to use.
There exists a need for effectively and holistically modeling and analyzing life cycle operations and support of missile and missile defense systems capturing the complexity, interdependencies, and random nature of the problem. Any approach should affordably provide accurate, reusable and portable models for analyzing the problem.